The present invention relates to a device and method for predicting odor pleasantness using an electronic nose.
In 1968, Dravnieks envisioned an artificial or electronic nose as an instrument that would inspect samples of odorous air and report the intensity and quality of an odor without the intervention of a human nose. Although eNoses have since been developed, they serve primarily in tasks of odor detection and discrimination but not for reporting odor quality.
The main component of an eNose is an array of non-specific chemical sensors. An odor analyte stimulates many of the sensors in the array and elicits a characteristic response pattern. The sensors inside eNoses can be made of a variety of technologies, but in all cases a certain physical property is measured and a set of signals is generated. The stages of the recognition process are similar to those of biological olfaction, where a sensor responds to more than one odorant and one odorant activates more than one sensor. Together, the set of activated sensors and their signals characterize the odor, sometimes referred as an odor fingerprint. Thus, an important difference between eNoses and analyte detectors such as gas chromatographs, is that whereas the latter are aimed at identifying the components that contribute to an odor, eNoses can be used to identify, as a whole, the mixture of components that together form an odor. Despite the promise of an artificial system that may substitute for olfaction, very few efforts have been made to use eNoses in tasks that go beyond detection and discrimination. A notable exception are the efforts to develop eNoses for medical diagnosis. In such efforts eNoses were used to identify the disease as a whole, rather than particular analytes that relate to it. In a previous proposal the present inventors linked eNose measurements to olfactory activity in olfactory receptor neurons suggesting that an eNose can capture the odor attributes relevant to biological receptors. Here we set out to ask whether eNose measurements can similarly be linked to olfactory perception. This effort may be more complicated than linking eNose output to receptor response. because perception is governed not only by stimulus structure, but also by higher-order mechanisms such as experience and learning.
To date, the only effort to report perceptual qualities using an eNose was by Burl et al (Burl et al. 2001). Using an array of conducting polymer composite detectors they predicted 17 odor qualities for each of 20 odorants by using a “leave one out” scheme, and a battery of prediction algorithms. Although significant prediction rates were obtained for a portion of the odor qualities, the result did not generalize to novel odorants. Burl et al (Burl et al. 2001) postulated that this outcome may have reflected the small number of odorants they used.
Burl et al (Burl et al. 2001) focused their efforts on predicting discreet perceptual characteristics, for example minty and floral.